我们从Python开源项目中，提取了以下9个代码示例，用于说明如何使用torch.optim.Rprop()。
def test_rprop(self): self._test_rosenbrock( lambda params: optim.Rprop(params, lr=1e-3), wrap_old_fn(old_optim.rprop, stepsize=1e-3) ) self._test_rosenbrock( lambda params: optim.Rprop(params, lr=1e-3, etas=(0.6, 1.1)), wrap_old_fn(old_optim.rprop, stepsize=1e-3, etaminus=0.6, etaplus=1.1) ) self._test_rosenbrock( lambda params: optim.Rprop(params, lr=1e-3, step_sizes=(1e-4, 3)), wrap_old_fn(old_optim.rprop, stepsize=1e-3, stepsizemin=1e-4, stepsizemax=3) ) self._test_basic_cases( lambda weight, bias: optim.Rprop([weight, bias], lr=1e-3) ) self._test_basic_cases( lambda weight, bias: optim.Rprop( self._build_params_dict(weight, bias, lr=1e-2), lr=1e-3) )
def r_prop(w, lr=0.01, etas=(0.5, 1.2), step_sz=(1e-06, 50)): return nn.Rprop(params=w, lr=lr, etas=etas, step_sizes=step_sz)
def get_optimizer(s): """ Parse optimizer parameters. Input should be of the form: - "sgd,lr=0.01" - "adagrad,lr=0.1,lr_decay=0.05" """ if "," in s: method = s[:s.find(',')] optim_params = {} for x in s[s.find(',') + 1:].split(','): split = x.split('=') assert len(split) == 2 assert re.match("^[+-]?(\d+(\.\d*)?|\.\d+)$", split[1]) is not None optim_params[split[0]] = float(split[1]) else: method = s optim_params = {} if method == 'adadelta': optim_fn = optim.Adadelta elif method == 'adagrad': optim_fn = optim.Adagrad elif method == 'adam': optim_fn = optim.Adam elif method == 'adamax': optim_fn = optim.Adamax elif method == 'asgd': optim_fn = optim.ASGD elif method == 'rmsprop': optim_fn = optim.RMSprop elif method == 'rprop': optim_fn = optim.Rprop elif method == 'sgd': optim_fn = optim.SGD assert 'lr' in optim_params else: raise Exception('Unknown optimization method: "%s"' % method) # check that we give good parameters to the optimizer expected_args = inspect.getargspec(optim_fn.__init__)[0] assert expected_args[:2] == ['self', 'params'] if not all(k in expected_args[2:] for k in optim_params.keys()): raise Exception('Unexpected parameters: expected "%s", got "%s"' % ( str(expected_args[2:]), str(optim_params.keys()))) return optim_fn, optim_params
def get_optimizer(model, s): """ Parse optimizer parameters. Input should be of the form: - "sgd,lr=0.01" - "adagrad,lr=0.1,lr_decay=0.05" """ if "," in s: method = s[:s.find(',')] optim_params = {} for x in s[s.find(',') + 1:].split(','): split = x.split('=') assert len(split) == 2 assert re.match("^[+-]?(\d+(\.\d*)?|\.\d+)$", split[1]) is not None optim_params[split[0]] = float(split[1]) else: method = s optim_params = {} if method == 'adadelta': optim_fn = optim.Adadelta elif method == 'adagrad': optim_fn = optim.Adagrad elif method == 'adam': optim_fn = optim.Adam optim_params['betas'] = (optim_params.get('beta1', 0.5), optim_params.get('beta2', 0.999)) optim_params.pop('beta1', None) optim_params.pop('beta2', None) elif method == 'adamax': optim_fn = optim.Adamax elif method == 'asgd': optim_fn = optim.ASGD elif method == 'rmsprop': optim_fn = optim.RMSprop elif method == 'rprop': optim_fn = optim.Rprop elif method == 'sgd': optim_fn = optim.SGD assert 'lr' in optim_params else: raise Exception('Unknown optimization method: "%s"' % method) # check that we give good parameters to the optimizer expected_args = inspect.getargspec(optim_fn.__init__)[0] assert expected_args[:2] == ['self', 'params'] if not all(k in expected_args[2:] for k in optim_params.keys()): raise Exception('Unexpected parameters: expected "%s", got "%s"' % ( str(expected_args[2:]), str(optim_params.keys()))) return optim_fn(model.parameters(), **optim_params)
